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Cited by 175

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping

Open access
Miller, Michael I., Beg, M. Faisal, Ceritoglu, Can, Stark, Craig·2005·Proceedings of the National Academy of Sciences

Abstract

The functional magnetic resonance imagery responses of declarative memory tasks in the medial temporal lobe (MTL) are examined by using large deformation diffeomorphic metric mapping (LDDMM) to remove anatomical variations across subjects. LDDMM is used to map the structures of the MTL in multiple subjects into extrinsic atlas coordinates; these same diffeomorphic mappings are used to transfer the corresponding functional data activation to the same extrinsic coordinates. The statistical power in the averaged LDDMM mapped signals is significantly increased over conventional Talairach–Tournoux averaging. Activation patterns are highly localized within the MTL. Whereas the present demonstration has been aimed at enhancing alignment within the MTL, this technique is general and can be applied throughout the brain.

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Metadata

Authors
Miller, M. I., Beg, M. F., Ceritoglu, C., & Stark, C.
Venue
Proceedings of the National Academy of Sciences
Published
2005-06-24
Volume
102
Issue
27
Pages
9685-9690
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2172210950
Cited by
175
References
8
FWCI
14.76
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. https://app.cochat.ai/papers/10.1073/pnas.0503892102
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Miller, M. I., Beg, M. F., Ceritoglu, C., & Stark, C. (2005). Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0503892102

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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